Small Language Models
Smaller models require less processing power and best used to perform smaller tasks. An example could be the retrieval of a specific collection of related information from within a VectorDB - further prompts (inputs or questions) and responses will likely be related and could therefore be actioned across that smaller database by an SLM. LLMs vs. SLMs: LLMs are still valuable for complex tasks or when a broader understanding of the web or your data is needed. The choice depends on the specific use case.
SLMs perform smaller tasks and require less computing power: This is a key advantage of SLMs. They are designed for specific tasks like code completion or summarizing text, making them efficient for local processing.
SLMs can be deployed on laptops: Due to their smaller size, SLMs can run on devices with less computational power compared to Large Language Models (LLMs).
Specialized Language Models (SLMs): While SLMs are generally smaller, some can be specialized for specific domains and tasks. These might be trained on focused datasets within the VectorDB you mentioned.